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analysis_na_fep.py
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analysis_na_fep.py
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import logging
import time
import tables
import sys
import os
import io, itertools
import pandas as pd
import numpy as np
import sympy
from dask_jobqueue import SLURMCluster
from dask.distributed import Client, LocalCluster
from dask import delayed
import alchemlyb
import mdsynthesis as mds
from alchemlyb.preprocessing import slicing
from alchemlyb.parsing.gmx import extract_dHdl
from alchemlyb.estimators import TI
logging.basicConfig(filename="analysis.get_dHdl_optimized.log", level=logging.INFO)
T = 310
k_b = 8.3144621E-3
sympy.init_printing(use_unicode=True)
r, theta, k_r, k_theta, r_0, theta_0, beta, V = sympy.symbols('r theta k_r k_theta r_0 theta_0 beta V')
subs = {k_theta: 23.0,
k_r: 16000.0,
r_0: 0.275,
theta_0: 0.0,
beta : 1/(8.3144621E-3 * 310),
V: 1.6605778811026237} # standard volume in nm^3
f_theta = sympy.exp(-(beta * k_theta/2) * (theta - theta_0)**2 ) * sympy.sin(theta)
Z_rtheta = sympy.Integral(f_theta, (theta, 0, sympy.pi))
Z_rtheta.subs(subs).evalf()
f_r = sympy.exp(-(beta * k_r/2) * (r - r_0)**2 ) * r**2
Z_rr = sympy.Integral(f_r, (r, 0, sympy.oo))
Z_rr.subs(subs).evalf()
Z_r = 2 * sympy.pi * Z_rtheta * Z_rr
Z_r.subs(subs).evalf()
DG_r = - 1/beta * sympy.ln(V/Z_r)
DG_r_unbind = DG_r.subs(subs).evalf()
DG_r_unbind = -17.8435335231149 # why keep computing the same thing
def bread(x, bsize=8192, stopat=None):
b = bytearray(bsize)
f = io.open(x, "rb")
for i in itertools.count():
numread = f.readinto(b)
if not numread:
break
if stopat and stopat < i:
break
return
def get_dHdl_XVG_delayed(xvg):
# TODO
# apply extract_dHdl_updated3
# merge get_header, extract_state
# cython for this?
# don't forget cache read by bytesio
fsize = os.path.getsize(xvg.abspath)
bufsize = 8192
stopat = fsize / bufsize / 2
s0 = time.time()
bread(xvg.abspath, bsize=bufsize, stopat=stopat)
s1 = time.time()
msg = ("{},{},{},{},{},{}".format('get_dHdl_XVG_delayed', 'bread',
xvg.abspath, s1-s0, s1, s0))
#print(msg)
logging.info(msg)
dHdl = extract_dHdl(xvg.abspath, T=T)
s2 = time.time()
msg = ("{},{},{},{},{},{}".format('get_dHdl_XVG_delayed',
'_extract_dHdl', xvg.abspath, s2-s1, s1, s0))
#print(msg)
logging.info(msg)
return dHdl
def slicing_delayed(dhdls, lower=None, upper=None, step=None):
return slicing(dhdls.sort_index(0), lower=lower, upper=upper, step=step)
def get_dHdl(sim, lower=None, upper=None, step=None):
try:
s = time.time()
dHdl = sim.data.retrieve('dHdl')
e = time.time()
if dHdl is None:
dHdl =
delayed(slicing_delayed)(delayed(pd.concat)([delayed(get_dHdl_XVG_delayed)(xvg)
for xvg in sim['WORK/dhdl/'].glob('*.xvg')]), lower=lower,
upper=upper, step=step)
else:
logging.info("get_dHdl,hdf5_read,{},{},{},{}".format(sim.name, e - s, s, e))
dHdl = slicing(dHdl.sort_index(0),
lower=lower,
upper=upper,
step=step)
except:
# THIS WILL NOT STORE THE VALUE FOR LATER USE SO YOU SHOULD REALLY
# CONTINUOUSLY UPDATE THE dHdl DATA IN THE SIMS
dHdl =
delayed(slicing_delayed)(delayed(pd.concat)([delayed(get_dHdl_XVG_delayed)(xvg)
for xvg in sim['WORK/dhdl/'].glob('*.xvg')]), lower=lower,
upper=upper, step=step)
return dHdl
def get_TI(dHdl):
ti = TI().fit(dHdl)
df = pd.DataFrame({'DG': k_b * T * ti.delta_f_.values[0,-1:],
'std': k_b * T * ti.d_delta_f_.values[0,-1:]},
columns=['DG', 'std'])
return df
if __name__ == "__main__":
cluster = SLURMCluster(cores=24,
processes=24,
memory='120GB',
walltime='00:59:00',
interface='ib0',
queue='compute',
death_timeout=60,
local_directory='/scratch/$USER/$SLURM_JOB_ID')
cluster.start_workers(96)
cl = Client(cluster)
#cl = LocalCluster()
ionsegs = {'repulsion_to_ghost':
mds.discover('/pylon5/mc3bggp/beckstei/Projects/Transporters/SYSTEMS/Na/repulsion_to_ghost/production1/'),
'ghost_to_ion':
mds.discover('/pylon5/mc3bggp/beckstei/Projects/Transporters/SYSTEMS/Na/ghost_to_ion/production1/')}
dHdls = {}
"""
for seg in ionsegs:
dHdls[seg] = [delayed(get_dHdl, pure=True)(sim, lower=5000, step=200)
for sim in ionsegs[seg]]
L_ionDG = {}
for seg in ionsegs:
iondg_d = delayed(get_TI)(delayed(pd.concat)(dHdls[seg]))
L_ionDG[seg] = cl.compute(iondg_d)
ionDG = cl.gather(L_ionDG)
dfs = []
for seg in ionDG:
df = ionDG[seg]
df['segment'] = seg
dfs.append(df)
ionDG = pd.concat(dfs)
ionDG = ionDG.set_index('segment')
ionDG.loc['ghost_to_ion', 'DG'] = -1 * ionDG.loc['ghost_to_ion', 'DG']
"""
topdir = mds.Tree('/oasis/scratch/comet/hrlee/temp_project/alchemlyb/')
segs_s2if = {'unrestrained_to_restrained': mds.discover(topdir['unrestrained_to_restrained/production1']),
'restrained_to_repulsion': mds.discover(topdir['restrained_to_repulsion/production1/']),
'repulsion_to_ghostrepulsion': mds.discover(topdir['repulsion_to_ghostrepulsion/production1/'])}
"""
segs_s4if = {'unrestrained_to_restrained': mds.discover(topdir['if/S4/unrestrained_to_restrained/production1']),
'restrained_to_repulsion': mds.discover(topdir['if/S4/restrained_to_repulsion/production1/']),
'repulsion_to_ghostrepulsion': mds.discover(topdir['if/S4/repulsion_to_ghostrepulsion/production1/'])}
segs_s2of = {'unrestrained_to_restrained': mds.discover(topdir['of/S2/unrestrained_to_restrained/production1']),
'restrained_to_repulsion': mds.discover(topdir['of/S2/restrained_to_repulsion/production1/']),
'repulsion_to_ghostrepulsion': mds.discover(topdir['of/S2/repulsion_to_ghostrepulsion/production1/'])}
segs_s4of = {'unrestrained_to_restrained': mds.discover(topdir['of/S4/unrestrained_to_restrained/production1']),
'restrained_to_repulsion': mds.discover(topdir['of/S4/restrained_to_repulsion/production1/']),
'repulsion_to_ghostrepulsion': mds.discover(topdir['of/S4/repulsion_to_ghostrepulsion/production1/'])}
"""
segs = {'s2if': segs_s2if}
""",
's4if': segs_s4if,
's2of': segs_s2of,
's4of': segs_s4of}
"""
L_DG = {}
for state in segs:
ddgs = {}
for seg in segs[state]:
#dHdls = delayed(pd.concat)([delayed(get_dHdl)(sim, lower=5000, step=200) for sim in segs[state][seg]])
dHdls = delayed(pd.concat)([get_dHdl(sim, lower=5000, step=200) for sim in segs[state][seg]])
ddgs[seg] = delayed(get_TI)(dHdls)
L_DG[state] = {seg: cl.compute(ddgs[seg]) for seg in segs[state]}
DG = cl.gather(L_DG)
state = 's2if'
dfs = []
for seg in DG[state]:
df = DG[state][seg]
df['segment'] = seg
dfs.append(df)
dfDG = pd.concat(dfs)
dfDG = dfDG.set_index('segment')
order = ['unrestrained_to_restrained',
'restrained_to_repulsion',
'repulsion_to_ghostrepulsion']
dfDG = dfDG.loc[order]
pd.concat([dfDG, ionDG])['DG'].sum()
std = pd.np.sqrt((pd.concat([dfDG, ionDG])['std'] **2).sum())
var = std**2
DG_unbind = (pd.concat([dfDG, ionDG])['DG'].sum() + DG_r_unbind)
"""
state = 's4if'
dfs = []
for seg in DG[state]:
df = DG[state][seg]
df['segment'] = seg
dfs.append(df)
dfDG = pd.concat(dfs)
dfDG = dfDG.set_index('segment')
order = ['unrestrained_to_restrained',
'restrained_to_repulsion',
'repulsion_to_ghostrepulsion']
dfDG = dfDG.loc[order]
pd.concat([dfDG, ionDG])['DG'].sum()
pd.np.sqrt((pd.concat([dfDG, ionDG])['std'] **2).sum())
DG_unbind = (pd.concat([dfDG, ionDG])['DG'].sum() + DG_r_unbind)
state = 's2of'
dfs = []
for seg in DG[state]:
df = DG[state][seg]
df['segment'] = seg
dfs.append(df)
dfDG = pd.concat(dfs)
dfDG = dfDG.set_index('segment')
order = ['unrestrained_to_restrained',
'restrained_to_repulsion',
'repulsion_to_ghostrepulsion']
dfDG = dfDG.loc[order]
pd.concat([dfDG, ionDG])['DG'].sum()
pd.np.sqrt((pd.concat([dfDG, ionDG])['std'] **2).sum())
DG_unbind = (pd.concat([dfDG, ionDG])['DG'].sum() + DG_r_unbind)
state = 's4of'
dfs = []
for seg in DG[state]:
df = DG[state][seg]
df['segment'] = seg
dfs.append(df)
dfDG = pd.concat(dfs)
dfDG = dfDG.set_index('segment')
order = ['unrestrained_to_restrained',
'restrained_to_repulsion',
'repulsion_to_ghostrepulsion']
dfDG = dfDG.loc[order]
pd.concat([dfDG, ionDG])['DG'].sum()
pd.np.sqrt((pd.concat([dfDG, ionDG])['std'] **2).sum())
DG_unbind = (pd.concat([dfDG, ionDG])['DG'].sum() + DG_r_unbind)
"""